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QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Overview of attention for article published in European Radiology Experimental, March 2023
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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7 X users

Citations

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3 Dimensions

Readers on

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15 Mendeley
Title
QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research
Published in
European Radiology Experimental, March 2023
DOI 10.1186/s41747-023-00326-z
Pubmed ID
Authors

Daniel Abler, Roger Schaer, Valentin Oreiller, Himanshu Verma, Julien Reichenbach, Orfeas Aidonopoulos, Florian Evéquoz, Mario Jreige, John O. Prior, Adrien Depeursinge

Abstract

Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data. QI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/ . Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 7%
Professor 1 7%
Student > Ph. D. Student 1 7%
Researcher 1 7%
Professor > Associate Professor 1 7%
Other 0 0%
Unknown 10 67%
Readers by discipline Count As %
Computer Science 2 13%
Arts and Humanities 1 7%
Nursing and Health Professions 1 7%
Medicine and Dentistry 1 7%
Unknown 10 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 May 2023.
All research outputs
#7,072,254
of 24,752,377 outputs
Outputs from European Radiology Experimental
#59
of 265 outputs
Outputs of similar age
#123,357
of 407,696 outputs
Outputs of similar age from European Radiology Experimental
#4
of 19 outputs
Altmetric has tracked 24,752,377 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 265 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 407,696 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.